Methods, apparatus, and systems to collect audience measurement data

ABSTRACT

Methods, apparatus, and systems to collect audience measurement data are disclosed. A disclosed example method includes collecting first program identification data and audience identification data during a first time period, the audience identification data being collected by prompting audience members in the monitored household to self-identify using a people meter, developing audience member behavior data based on the first program identification data and the audience identification data collected in the first time period, collecting second program identification data in a second time period after the first time period without collecting audience identification data, and identifying the audience members associated with the second program identification data based on the audience member behavior data.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to audience measurement, andmore particularly, to methods, apparatus, and systems to collectaudience measurement data.

BACKGROUND

Determining the size and specific demographics of a television viewingaudience helps television content providers and distributors scheduletelevision programming and determine a price for advertising during theprogramming. In addition, accurate estimates of television viewingdemographics enable advertisers to target certain types and sizes ofaudiences. To collect these demographics, an audience measurementcompany enlists a plurality of television viewers to cooperate in anaudience measurement study for a predefined length of time. The viewinghabits and demographic data associated with these enlisted viewers iscollected and used to statistically determine the size and demographicsof a television viewing audience. In some examples, automaticmeasurement systems may be supplemented with survey information recordedmanually by the viewing audience members.

The process of enlisting and retaining participants for purposes ofaudience measurement may be a difficult and costly aspect of theaudience measurement process. For example, participants are typicallycarefully selected and screened for particular characteristics so thatthe population of participants is representative of the overall viewingpopulation. Additionally, the participants are required to performspecific tasks that enable the collection of the data, such as, forexample, periodically self-identifying while viewing televisionprogramming.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a diagram of a monitored household with a people meter.

FIG. 2 shows a diagram of the monitored household of FIG. 1 without apeople meter.

FIG. 3 shows a diagram of the monitored audience members of FIGS. 1and/or 2 without a base metering device.

FIG. 4 shows a diagram of the monitored audience members with a homenetwork and a computer.

FIG. 5 shows an example functional diagram of the example base meteringdevice used to collect program identification data viewed by themonitored households of FIGS. 1, 2, and 4.

FIG. 6 shows a table of example audience identification data and programidentification data collected by the example based metering device ofFIG. 5.

FIG. 7 shows a table of behavior data compiled by the example basemetering device of FIG. 5 during a time period.

FIGS. 8A, 8B, 9A, 9B, and 10 are flowcharts of examplemachine-accessible instructions, which may be executed to implement thepeople meter or the example base metering device of FIGS. 1-5.

FIG. 11 is a schematic illustration of an example processor platformthat may be used and/or programmed to execute the example processesand/or the example machine-accessible instructions of FIGS. 8A, 8B, 9A,9B, and 10 to implement any or all of the example methods, apparatusand/or articles of manufacture described herein.

DETAILED DESCRIPTION

Example methods, apparatus, systems, and articles of manufacture tocollect audience measurement data are disclosed. A disclosed examplemethod includes collecting first program identification data andaudience identification data during a first time period. The audienceidentification data is collected by prompting audience members in themonitored household to self-identify using a people meter and developingaudience member behavior data based on the first program identificationdata and the audience identification data collected during the firsttime period. The example method also includes collecting second programidentification data in a second time period after the first time periodwithout collecting audience identification data and identifying theaudience members associated with the second program identification databased on the audience member behavior data.

A disclosed example system to collect audience measurement data includesa set of active audience metering systems. The active audience meteringsystems are located in respective households and include a respectivebase metering device to collect program identification data and arespective people meter to collect audience identification data.Further, the disclosed example system includes a set of legacy audiencemetering systems. Each of the legacy audience metering systems hasformerly been associated with an active audience metering system.Additionally, each of the legacy audience metering systems is located ina respective household and includes a respective meter to collectprogram identification data.

The example system disclosed herein also includes a collection server toreceive program identification data and audience identification datafrom the set of active audience metering systems and programidentification data from the set of legacy audience metering systems.The collection server adjusts the program identification data from theset of legacy audience metering systems based on the programidentification data and audience identification data from the set ofactive audience metering systems. Additionally or alternatively, thecollection server may adjust the program identification data from theset of legacy audience metering systems based on program identificationdata and audience identification data from the legacy audience meteringsystems during the time period that those systems included peoplemeters. The collection system uses this collected data to generateviewership statistics for advertisers, television program producers, andtelevision content providers.

While the following disclosure is made with respect to exampletelevision systems, it should be understood that the disclosed system isreadily applicable to many other media systems (e.g., radio, Internet,mobile devices, etc.). Accordingly, while the following describesexample systems and methods, persons of ordinary skill in the art willreadily appreciate that the disclosed examples are not the only way toimplement such systems.

Currently, an audience measurement company enlists a plurality oftelevision viewers (e.g., panelists or audience members) to cooperate inan audience measurement study for a predefined length of time. Theaudience measurement company monitors viewing habits of these enlistedviewers via a base metering device and a people meter. The base meteringdevice and the people meter identify what activity occurs at the machinelevel (e.g., which television channel is being watched) and identify thepresence of specific people viewing the television. The people meter isan electronic device that is typically disposed in a viewing area of amonitored household and is proximate to one or more of the viewers. Thepeople meter communicates with the base metering device, which measuresvarious signals associated with a television for a variety of purposesincluding, but not limited to, determining the operational status of thetelevision, (i.e., whether the television is off or on) and identifyingprogramming displayed by the television. The base metering device alsocollects and stores program identification data that is associated withviewed programming. Program identification data includes, for example, atitle of a television program, a genre of a television program, names ofactors in a television program, a channel broadcasting a televisionprogram, a time and/or day a television program is viewed, and/or anyother type of information associated with a television program. Theprogram identification data may be collected directly or, alternatively,via identifiers that are supplemented with reference data collectedseparately and combined though additional processing.

Additionally, many known audience measurement companies collectdemographic data about enlisted viewers to statistically determine thesize and demographics of a television viewing audience. The demographicdata includes, for example, age, gender, income level, educationallevel, marital status, geographic location, race, etc. The demographicdata is typically collected prior to an audience measurement companyinstalling and/or activating a base metering device within a householdto be monitored.

To correlate the collected program identification data with demographicdata, audience measurement companies often utilize a people meter thatperiodically prompts viewers to self-identify. The people meter promptsaudience members based on any number of triggers, including, for examplea channel change or an elapsed period of time. Additionally, the peoplemeter may prompt the audience members to input information by depressingone of a set of buttons, each of which is assigned to represent adifferent household member. Alternatively, audience members mayself-identify by entering an identification code and/or their name intothe people meter. For example, the people meter may prompt the audiencemembers to register (i.e., log in) and/or prompt the audience members toindicate they are still present in the viewing audience. The peoplemeter then forwards this audience identification data to the basemetering device, which then combines the audience identification datawith collected program identification data.

Typically, a household agrees to be monitored for a time period (e.g.,two years). Although periodically inputting information in response to aprompt may not be burdensome when required for an hour, a day or even aweek or two, some participants become weary of the prompting and datainput tasks over longer periods of time (e.g., button pushing fatigue).Thus, after the monitoring time period has expired, many householdschoose to have the base metering device and people meter removed.

The example methods, apparatus, systems, and articles of manufacturedescribed herein provide an incentive for households to remain monitoredafter the time period by removing the people meter, thereby eliminatingongoing prompts for audience members to self-identify. Three relativelysignificant cost drivers for operating audience measurement panelsinclude identifying audience members, providing incentives for audiencemembers to participate in the monitoring, and installing a people meterand/or a base metering device. By retaining households to monitor, anaudience measurement company can save resources by not having to screenand/or select new households. Extended retention of households,particularly when relatively smaller incentives are required, enables anaudience measuring company to more efficiently increase a sample size ofmonitored households. Additionally, by retaining households, an audiencemeasurement company compiles longer time periods of data from the samehousehold that may be used to identify how viewing habits of the samehousehold change over time.

In addition, the example methods, apparatus, systems, and articles ofmanufacture described herein provide an audience measurement companyflexibility for monitoring households after an initial time period. Forexample, after a time period of collecting audience measurement data andprogram identification data, the people meter and the base meteringdevice is replaced with a mailable portable meter. Alternatively, afterthe time period, a service provider of the programming may send programidentification data directly to the audience measurement company insteadof the household having meters. In yet another example, data collectedfrom the people meter and the base metering device may be used by theaudience measurement company to monitor Internet usage and determinewhich audience members are using the Internet.

The example methods, apparatus, systems, and articles of manufacturedescribed herein also provide an audience measurement company costflexibility to monitor different regions. For example, broadcasters in aregion may desire 500 households to be monitored but only have arelatively small budget. The example methods, apparatus, systems, andarticles of manufacture described herein are utilized such that a firstgroup of households is configured with a relatively more expensiveactive prompt people meter and base metering device, while a secondgroup of households is configured with a relative less expensive passivebase metering device. The example audience measurement company compilesdata from the two groups and uses the active prompt data to determinewhich of the audience members from the passive group are most likelywatching the monitored programming.

The example methods and apparatus, systems, and articles of manufacturedescribed herein retrain monitored households by collecting firstprogram identification data and audience identification data during afirst time period. During this first time period, the audienceidentification data is collected by requiring audience members in themonitored household to self-identify using the people meter. The examplemethods, apparatus, and systems, and articles of manufacture describedherein then generate audience member behavior data based on the firstprogram identification data and the audience identification datacollected in the first time period. After this first time period, thepeople meter is removed and second program identification data during asecond time period is collected without collecting audienceidentification data. Because audience members no longer self-identifyduring the second time period, audience members associated with thesecond program identification data are identified based on the audiencemember behavior data complied during the first time period.

In the example of FIG. 1, a media system 100 including a media serviceprovider 102, a television 104 is metered using an audience measurementsystem 106 having a base metering device 108 and a people meter 110. Thetelevision 104 is positioned in a viewing or media consumption area 112located within a house 114 occupied by one or more people, referred toas audience members 116, all of whom have agreed to participate in anaudience measurement research study. The viewing area 112 includes thearea in which the television 104 is located and from which thetelevision 104 is viewed by one or more audience members 116 located inthe viewing area 112.

The media service provider 102 of the illustrated example is implementedusing any media service provider 102 such as, but not limited to, acable media service provider 118, a radio frequency (RF) media provider120, and/or a satellite media service provider 122. The television 104receives a plurality of signals transmitted via a plurality of channelsby the media service provider 102 displays signals provided in anyformat such as, for example, an National Television Standards Committee(NTSC) television signal format, a high definition television (HDTV)signal format, an Advanced Television Systems Committee (ATSC)television signal format, a phase alternation line (PAL) televisionsignal format, a digital video broadcasting (DVB) television signalformat, an Association of Radio Industries and Businesses (ARIB)television signal format, etc. The processing performed by thetelevision 104 includes, for example, extracting a video componentdelivered via the received signal and an audio component delivered viathe received signal, causing the video component to be displayed on ascreen/display associated with the television 104, and causing the audiocomponent to be emitted by speakers associated with the television. Theprogramming content contained in the media signal includes, for example,a television program, a movie, an advertisement, a video game, a radioprogram, and/or a preview of other programming that is or will beoffered by the media service provider 102.

The example base metering device 108 of FIG. 1 is configured as astationary device disposed on or near the television 104 and performsone or more of a variety of television metering methods. Depending onthe types of metering that the base metering device 108 is to perform,the base metering device 108 can be physically coupled to the television104 or may instead be configured to capture signals emitted externallyby the television 104 such that direct physical coupling to thetelevision 104 is not required. A base metering device 108 is providedfor each television 104 or other monitored media device disposed in thehousehold 114, such that the base metering devices 108 captures dataregarding all in-home media viewing or consumption by the audiencemembers 116. In an example, the base metering device 108 is implementedas a low-cost electronic device (e.g., a mailable meter) that is shippedto the viewer's home 114 (e.g., via regular mail) and installed by theaudience member 116 by, for example, plugging the base metering device108 into a commercial power supply, i.e., an electrical outlet.

The base metering device 108 of the illustrated example communicateswith a remotely located central data collection facility 124 via anetwork 126. The network 126 is implemented using any type of public orprivate network such as, but not limited to, the Internet, a telephonenetwork, a local area network (LAN), a cable network, and/or a wirelessnetwork. To enable communication via the network 126, the base meteringdevice 108 includes a communication interface that enables a connectionto an Ethernet, a digital subscriber line (DSL), a telephone line, acoaxial cable, or any wireless connection, etc. The example basemetering device 108 sends program identification data and/or audienceidentification data to the central data collection facility 124periodically and/or upon a request by the collection facility 124. Thecentral data collection facility 124 includes a server 128 and adatabase 130. Further, the central data collection facility 124processes and stores data received from the base metering device 108.

The example collection facility 124 of FIG. 1 combines audienceidentification data and program identification data from multiplehouseholds 114 to compile statistical viewing data. Additionally, thecollection facility 124 integrates demographic data with the compiledviewing data to generate demographic statistical information. Theexample collection facility 124 receives the demographic informationwhen the audience members 116 register and/or sign-up to be monitored.The collection facility 124 generates reports for advertisers and/orprogram producers based on the compiled statistical data.

In the example illustrated example of FIG. 1, the audience measurementsystem 106 includes the example people meter 110 disposed in the viewingarea 112. The example people meter 110 includes a set of buttons (notshown). Each button may be assigned to represent a single, different oneof the audience members 116 residing within the household 114.Alternatively, the buttons may enable the audience members 116 to entercorresponding identification data (e.g., a name). The people meter 110periodically prompts the audience members 116, via a set of LEDs, adisplay screen, and/or an audible tone, to indicate that they arepresent in the viewing area 112 by pressing an assigned button. Todecrease the number of prompts, and thus the number of intrusionsimposed upon the television watching experience of the audience members116, the base metering device 108 instructs the people meter 110 toprompt only when unidentified audience members 114 are located in theviewing area 112 and/or to prompt only after the base metering device108 detects a channel change and/or a change in state of the television104. In other examples, the base metering device 108 may include atleast one sensor and/or be communicatively coupled to at least onesensor that detects a presence of the audience members 116 in theviewing area 112.

The example people meter 110 is implemented as a separate device that iscommunicatively coupled to the base metering device 108 or,alternatively, may be implemented as an integral part of the basemetering device 108. In the example of FIG. 1, the audience members 116agree for an audience measurement company to install the base meteringdevice 108 and the people meter 110. Further, the audience members 116agree to have the base metering device 108 and the people meter 110collect program identification data and audience identification data fora time period. For example, the audience members 116 may agree to bemonitored for two years. During these two years, the base meteringdevice 108 stores program identification data associated with televisionprogramming viewed by the audience members 116. Additionally, the peoplemeter 110 prompts the audience members 116 to self-identify. The peoplemeter 110 transmits the identities of the audience members 116 (e.g.,audience identification data) to the base metering device 108. The basemetering device 108 then combines the program identification data andthe audience identification data to create behavior data for each of theaudience members 116. The behavior data is used by the base meteringdevice 108 to correlate each of the audience members 116 to viewingtrends or patterns over the two year time period. The example basemetering device 108 periodically generates behavior data or,alternatively, generates behavior data after a predefined amount ofprogram identification data has been collected.

FIG. 2 shows a diagram of the monitored audience members 116 without theexample people meter 110 of FIG. 1. In other examples, the people meter110 is deactivated, thereby eliminating prompts to the audience members116 to self-identify. The people meter 110 is removed and/or deactivatedafter an expiration of a first time period. After this first timeperiod, the audience members 116 agree to be monitored by the basemetering device 108 without having to self-identify via the people meter110. In some examples, the audience members 116 are more willing tocontinue to be monitored when the people meter 110 is removed. In theseexamples, the audience members 110 become weary of periodicallyself-identifying. Because the base metering device 108 passivelymonitors the television 104 (e.g., without requiring interaction withthe audience members 116), the audience members 116 may agree to retainthe base metering device 108 for a second time period. In other words,the utilization of the behavior data by the base metering device 108extends a panelist term of the audience members 116 by eliminatingpeople meter 110 button pushing fatigue.

During this second time period, the base metering device 108 collectsprogram identification data associated with programs viewed by theaudience members 116. However, because the audience members 116 are notself-identifying, the example base metering device 108 uses the behaviordata compiled during the first time period to determine the identitiesof the audience members 116 viewing the programming. The example basemetering device 108 determines which of the audience members 116 areviewing programming by matching program identification data to thecompiled behavior data for each of the audience members 116. Forexample, the base metering device 108 identifies program identificationdata of a currently viewed program. This program identification dataincludes a day (e.g., Thursday), a time (e.g., 9:00 P.M.), a title,(e.g., The Office), a genre (e.g., comedy), and/or a broadcast channel(e.g., NBC). The base metering device 108 partitions the audienceidentification data into categories and matches the data tocorresponding data in the same category within the behavior data. Thus,if the behavior data of the audience member 116 includes at least somematches (e.g., The Office, Thursday, 9:00 P.M., comedy, NBC), the basemetering device 108 infers that the audience member 116 is currentlyviewing the programming. The base metering device 108 then storesaudience identification data associated with the determined oridentified audience member(s) with the program identification data ofthe currently viewed program.

In other examples, the base metering device 108 of FIG. 2 utilizesstatistics to determine which of the audience members 116 are viewingthe programming. A statistical analysis may include, for example, aNaïve Bayes analysis, a regression analysis, a fuzzy logic algorithm, ananalysis of variance test, and/or any other statistical algorithm. In aNaïve Bayes analysis, for example, the base metering device 108 utilizesthe behavior data collected during the first time period with the peoplemeter 110 as a training set used to calculate a probability that each ofthe audience members 116 is currently viewing programming.

Alternatively, the examples in FIG. 1 and FIG. 2 may represent twodifferent households during a time period. For example, FIG. 1 shows theaudience measurement system 106 as an active metering system includingthe people peter 110 and the base metering device 108. The audiencemeasurement system 106 of FIG. 1 collects audience measurement data viathe people meter 110 and program identification data via the basemetering device 108.

Additionally, FIG. 2 shows the audience measurement system 106 as alegacy audience metering system including the base metering device 108.The audience measurement system 106 of FIG. 2 collects programidentification data via the base metering device 108. Further, theaudience measurement system 106 of FIG. 2 has collected audienceidentification data via the people meter 110 during a previous timeperiod.

The example collection facility 124 of FIGS. 1 and 2 collects theaudience identification data from the house 114 of FIG. 1 and therespective program identification data from the house 114 of FIGS. 1 and2. The example collection facility 124 uses the audience identificationdata and the program identification data from the house 114 of FIG. 1 todetermine which of the audience members 116 of FIG. 2 viewed programmingassociated with the programming identification data. The collectionfacility 124 determines the audience members 116 of FIG. 2 by matching,adjusting, and/or correlating demographic information to the audiencemembers 116 of FIG. 1.

Alternatively, the collection facility 124 may use audienceidentification data and program identification data collected during theprevious time period from the house 114 of FIG. 2 to determine which ofthe audience members 116 watched which programming during the currenttime period. In yet another example, the collection facility 124 may usethe audience identification data and the program identification datacollected during the previous time period from the house 114 of FIG. 2and the audience identification data and the program identification datafrom the house 114 of FIG. 1 to determine which of the audience members116 watched which programming during the current time period.

FIG. 3 shows a diagram of the monitored audience members 116 without theexample base metering device 108 of FIGS. 1 and/or 2. In this example,the audience members 116 agree to have program identification data sentdirectly from the cable media service provider 118. In other examples,the program identification data is sent from the RF media provider 120and/or the satellite media service provider 122 to the collectionfacility 124.

The audience members 116 agree to have the base metering device 108removed and/or deactivated with the people meter 110 after the timeperiod of self-identifying. However, the audience members 116 agree tohave program identification data sent to the collection facility 124without having measurement components within the house 114. In thismanner, the audience members 116 are still monitored by the collectionfacility 124 of the audience measurement company without havingmeasurement components (e.g., the base metering device 108 and thepeople meter 110) within the audience measurement system 106. Theexample collection facility 124 correlates the program identificationdata from the service provider 118 with the already collected programidentification data and the audience identification data to determinewhich of the audience members 116 viewed the programming.

In other examples, the base metering device 108 is replaced with apassive mailable meter that collects program identification data. Inthese examples, the audience members 116 setup the mailable meter inproximity to the television 104 to record program identification data.In yet other examples, the base metering device 108 is replaced withportable meters that are worn and/or carried by the audience members116. In these examples, the portable meters collect programidentification data when the audience member 116 is within the viewingarea 112.

FIG. 4 shows a diagram of the monitored audience members 116 including ahome network 402 and a computer 404. In this example, the people meter110 is removed after the first time period, similar to the example shownin FIG. 2. Additionally, after the first time period, the audiencemembers 116 agree to have their Internet usage monitored. To monitorInternet usage, an audience measurement company installs a softwaremonitoring application 406 on the computer 404. In other examples, thecollection facility 124 transmits the monitoring application 406 to thecomputer 404. In yet other examples, the audience member 116 installsthe monitoring application 406 on the computer 404. In yet otherexamples, the base metering device 108 monitors the Internet usage ofthe audience members 116.

The example computer 404 in FIG. 4 is shown communicatively coupled tothe base metering device 108 via the home network 402. In otherexamples, the computer 404 may be coupled to the network 126 via thehome network 402 and/or via a network gateway (not shown).Alternatively, the computer 404 may be replaced by a cell phone, asmartphone, a laptop, or a netpad, and/or any other device capable ofconnecting to the Internet. The example home network 402 of theillustrated example includes a Local Area Network (LAN), wireless LAN(WLAN), Virtual Private Network (VPN), and/or any other network.

An audience measurement company collects and/or combines Internet usagedata with television viewing data to generate statistical reports basedon different media types. In the example of FIG. 4, the monitoringapplication 406 on the computer 404 and/or the base metering device 108monitors Internet usage of the audience members 116. Because theaudience members 116 are generally weary of self-identifying whileviewing Internet content, the base metering device 108 uses behaviordata collecting during the first time period to determine which of theaudience members 116 is using the computer 404. In other examples, themonitoring application 406 utilizes the behavior data stored on the basemetering device 108 and/or stored at the collection facility 124.

The audience members 116 are identified by matching Internet usagebehavior to the behavior data associated with, for example, televisionprogramming viewing. Because the behavior data (e.g., audienceidentification data combined with program identification data) of theaudience members 116 is collected over a relatively long time period,the monitoring application 406 and/or the base metering device 108matches Internet usage to patterns within the behavior data. Forexample, the audience member 116 that navigates to ESPN.com is matchedby the monitoring application 406 and/or the base metering device 108 totelevision behavior data that includes the channel ESPN and/or moregenerally, a sports genre. Similarly, the audience member 116 that postsmessages on a Facebook Internet application regarding home improvementsmay be matched by the monitoring application 406 and/or the basemetering device 108 to behavior data that includes home improvementtelevision programs and/or channels (e.g., HGTV). Thus, the monitoringapplication 406 and/or the base metering device 108 collects Internetusage data, determines which of the audience members 116 navigated to anInternet site corresponding to the usage data, and transmits thecollected data to the collection facility 124.

The collection facility 124 combines the Internet usage data and programidentification data collected during a second time period subsequent tothe first time period and adjusts and/or combines this data with programidentification data and audience identification data collected duringthe first time period. Further, the Internet usage data is combined withthe television data to generate behavior data for each of the audiencemembers 116 that is used for statistical analysis and/or marketingreports.

FIG. 5 shows an example functional diagram of the example base meteringdevice 108 used to collect program identification data viewed by theaudience members 116 of FIGS. 1, 2, and 4. The example base meteringdevice 108 includes a processor 502 to control the operation of the basemetering device 108 in a manner that enables the functionality describedherein. For example, the processor 502 collects and stores programidentification data based on storage criteria (e.g., time period, day,session, audience member, etc.). Additionally, the processor 502communicates with the collection facility 124, the people meter 110,and/or the computer 404.

To determine when the first time period expires, the example processor502 includes a timer 503. The example timer 503 indicates to theprocessor 502 when the first time period ends and/or when the secondtime period ends. Upon receiving a notification of an expiration of thefirst time period from the timer 503, the example processor 502 stopsreceiving audience identification data from the people meter 110. Inother examples, the base metering device 108 may sense when the peoplemeter 110 is deactivated and/or removed and resets the timer 503 tostart a second time period. Additionally, upon an expiration of a timeperiod on the timer 503, the processor 502 transmits behavior data,audience identification data, program identification data, demographicdata, and/or Internet usage data to the to the collection facility 124.

To store instructions utilized by the example processor 502 and/or tostore audience identification data, program identification data,behavior data, demographic data, and/or Internet usage data, the basemetering device 108 includes a memory 504. The example memory 504 may beimplemented as a programmable gate array, an application specificintegrated circuit (ASIC), an erasable programmable read only memory(EPROM), a read only memory (ROM), a random access memory (RAM), amagnetic media, an optical media and/or any other suitable type ofmedium.

The base metering device 108 of the illustrated example also includes acommunication interface 506 that enables communication between the basemetering device 108 and the remotely located central data collectionfacility 124 via the network 126. For example, the communicationinterface 506 is implemented using any, communication interface capableof enabling communication with the central data collection facility 124via the network 126 including for example, an Ethernet card, a digitalsubscriber line, a coaxial cable, or any wireless connection. Thecommunication interface 506 also enables communication with themonitoring application 406 on the computer 404 via the home network 402.

The example communication interface 506 of FIG. 5 periodically transmitsaudience identification data, program identification data, behaviordata, and/or demographic data stored within the memory 504 to thecollection facility 124 via the network 126. Alternatively, thecommunication interface 506 may receive a request from the collectionfacility 124 to transmit the collected data. The example communicationinterface 506 of the illustrated example also transmits status and/ordiagnostic information associated with the operation of the basemetering device 108 to the collection facility 124.

To enable the transfer of audience identification data from the peoplemeter 110, the example base metering device 108 of FIG. 5 includes apeople meter interface 508. The example people meter interface 508receives audience identification data from the people meter 110 andforwards the data to the processor 502. Additionally, the people meterinterface 508 sends an instruction to the people meter 110 to prompt theaudience members 116 to self-identify.

The example base metering device 108 also includes a user interface 510that enables the audience members 116 to provide information directly tothe base metering device 108. This information includes registrationdata to configure the base metering device 108 within the audiencemeasurement system 106 and/or demographic data for each of the audiencemembers 116. In some examples, the user interface 510 includes, forexample, a keyboard, touchpad, and/or keypad. Upon receiving informationfrom the audience members 116, the user interface 510 transmits theinformation to the processor 502.

The user interface 510 displays this received information to theaudience members 510 via a display 512. The display 512 receives dataand/or information from the processor 502. In some examples, the display512 provides registration instructions and/or prompts audience members116 to provide demographic information. Additionally, if the processor502 detects an error associated with the audience measurement system106, the processor 502 provides troubleshooting instructions via thedisplay 512.

To collect program identification data, the example base metering device108 of FIG. 5 includes television programming measurement circuitry 514.The example television programming measurement circuitry 514 includesany hardware, meters, and/or software for detecting programmingidentification data from the service providers 118-122 displayed by thetelevision 104. In some examples, the television programming measurementcircuitry 514 receives program identification data from the serviceproviders 118-122. In other examples, the television programmingmeasurement circuitry 514 identifies program identification data fromembedded and/or encoded signals within the programming. In yet otherexamples, the television programming measurement circuitry 514determines program identification data from audio and/or video input toand/or output from the television 104.

The example television programming measurement circuitry 514 of theillustrated example transmits the determined program identification datato the processor 502. The example processor 502 then combines theprogram identification data with audience identification data receivedfrom the people meter interface 508 and stores the data within thememory 504. In this manner, the processor 502 links viewed programmingwith the audience members 116 that viewed the programming.

The example base metering device 108 of FIG. 5 also includes one or moresensors 516 to detect the audience members 116 within the viewing area112. The sensors 516 include, for example, motion sensors, heat sensors,infrared object detection sensors, etc. The example processor 502 usesthe number of individuals detected by the sensors 516 to ensure each ofthe detected audience members 116 self-identifies via the people meter110. Additionally, the example processor 502 uses a number of thedetected audience members 116 from the sensors 516 to determine how manyof the audience members 116 are within the viewing area 112 during thesecond time period when the people meter 110 is removed and/ordeactivated. For example, if the sensors 516 detect two people, theprocessor 502 determines which of the two audience members 116 are inthe viewing area 112 based on behavior data collected during the firsttime period with the people meter 110.

To process audience identification data and/or program identificationdata, the example base metering device 108 of FIG. 5 includes a behaviorprocessor 518. The example behavior processor 518 generates behaviordata based on the audience members 116 self-identifying while viewingprogramming during a first time period. During the first time period,the behavior processor 518 receives program identification data andcorresponding audience identification data from the processor 502. Thebehavior processor 518 accesses the memory 504 for compiled behaviordata for each of the audience member 116 and stores the received data tothe appropriate partition within the memory 504 associated with theaudience member(s) 116. Additionally, the example behavior processor 518periodically compiles and/or summarizes the behavior data for each ofthe audience members 116 based on viewing trends and/or patterns.

During a second time period when the people meter 110 is removed and/ordeactivated, the example behavior processor 518 receives programidentification data from the processor 502 and determines which of theaudience members 116 viewed the corresponding programming based on thebehavior data. For example, the behavior processor 518 matches a day, atime, a program title, a program genre, and/or a broadcast channel tocorresponding behavior data. The behavior processor 518 then determinesthat the audience member(s) 116 with a number of matching categoriesabove a threshold are currently viewing the programming. In otherexamples, the behavior processor 518 implements a statistical algorithm(e.g., fuzzy logic, Naïve Bayes, regression analysis, etc.) to determinewhich of the audience member(s) 116 are most likely viewing currentprogramming based on patterns within the behavior data. The behaviorprocessor 518 then links the determined audience members 116 with thereceived program identification data and stores this information to thememory 504.

The example behavior processor 518 of the illustrated example alsodetermines during a second time period which of the audience members 116are navigating the Internet based on Internet usage data received fromthe communication interface 506. For example, the monitoring application406 on the computer 404 of FIG. 4 transmits Internet usage data to thebase metering device 108. The communication interface 506 transmits theInternet usage data to the behavior processor 518 via the processor 502.The behavior processor 518 then matches Internet usage data to patternswithin the behavior data for each of the audience members 116. In someexamples, the behavior processor 518 may analyze and/or compare trendsin the navigation history of the Internet usage data to viewing patternsof the behavior data. Alternatively, the behavior processor 518 maymatch portions of the Internet usage data to corresponding categories ofthe behavior data. Further, during the first time period, the behaviorprocessor 518 generates behavior data based partially on Internet usagedata and Internet audience identification data in instances when themonitoring application 406 prompts the audience members 116 toself-identify using the computer 404.

While an example manner of implementing the base metering device 108 ofFIGS. 1-4 has been illustrated in FIG. 5, one or more of the elements,processes and/or devices illustrated in FIG. 5 may be combined, divided,re-arranged, omitted, eliminated and/or implemented in any other way.Further, the example processor 502, the example memory 504, the examplecommunication interface 506, the example people meter interface 508, theexample user interface 510, the example display 512, the exampletelevision programming measurement circuitry 514, the example sensors516, the example behavior processor 518 and/or, more generally, theexample base metering device 108 of FIG. 5 may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the exampleprocessor 502, the example memory 504, the example communicationinterface 506, the example people meter interface 508, the example userinterface 510, the example display 512, the example televisionprogramming measurement circuitry 514, the example sensors 516, theexample behavior processor 518 and/or, more generally, the example basemetering device 108 could be implemented by one or more circuit(s),programmable processor(s), application specific integrated circuit(s)(ASIC(s)), programmable logic device(s) (PLD(s)) and/or fieldprogrammable logic device(s) (FPLD(s)), etc. When any of the appendedapparatus claims are read to cover a purely software and/or firmwareimplementation, at least one of the example processor 502, the examplememory 504, the example communication interface 506, the example peoplemeter interface 508, the example user interface 510, the example display512, the example television programming measurement circuitry 514, theexample sensors 516, and/or the example behavior processor 518 arehereby expressly defined to include a computer readable medium such as amemory, DVD, CD, etc. storing the software and/or firmware. Furtherstill, the example base metering device 108 of FIG. 5 may include one ormore elements, processes and/or devices in addition to, or instead of,those illustrated in FIG. 5, and/or may include more than one of any orall of the illustrated elements, processes and devices.

FIG. 6 shows a table 600 of example audience identification data andprogram identification data collected by the example based meteringdevice 108 of FIGS. 1, 2, 4, and/or 5. The example base metering device108 collects this data in the table 600 during the first time periodwhen the people meter 110 prompts the audience members 116 toself-identify. In the example of FIG. 6, the table 600 includes data fora session (e.g., Tuesday Feb. 16, 2010). The column of the table 600labeled Self-Identified Audience Members includes audienceidentification data. The columns labeled Time Period, Title, Genre andChannel include program identification data. In other examples, the basemetering device 110 may collect other types of program identificationdata including, but limited to, actors of the program, a director of theprogram, a live/taped status of the program, a repeat/first run statusof the program, advertisements shown during breaks in the program, etc.

In an example of FIG. 6, during the 8:00-9:00 P.M. time period, the basemetering device 108 collects program identification data associated withthe program Law and Order—LVU that is shown by the television 104. Alsoduring this 8:00-9:00 time period, the example people meter 110 promptsfor any of the audience members 116 to self-identify. In this example,the audience member associated with the BF01 identifier uses the peoplemeter 110 to self-identify. The base metering device 108 continues tocollect program identification data and combine this data with audienceidentification data transmitted from the people meter 110 for the othertimes (e.g., 9:00-9:30 P.M., 9:30-10:00 P.M., 10:00-11:00 P.M., and11:00-11:20 P.M.).

FIG. 7 shows a table 700 of behavior data compiled by the example basemetering device 108 of FIGS. 1, 2, 4, and/or 5 during the first timeperiod. The first time period shown in the table 700 is from Jan. 15,2009 through Jan. 15, 2011. During this time period, the example basemetering device 108 collects and compiles audience identification dataand program identification data as shown in conjunction with the table600 in FIG. 6. Periodically during this time period and/or after thisfirst time period, the base metering device 108 generates the behaviordata shown in the table 700 based on data within tables similar to thetable 600.

In the example of FIG. 7, the table 700 includes the audience memberidentifiers BF01, DF01, and SF01 that correspond to the audience members116. The example table 700 also includes a summary of times, days,program titles, genres, and channels frequently viewed by each of theaudience members 116. Additionally, the Titles, Genres and Channelscolumns of the table 700 are not necessarily aligned with the Times andDays columns. For example, the table 700 shows that the BF01 audiencemember watched the most television programming from 8:00-9:00 P.M. onMondays, Tuesdays and Wednesdays and watched the second most televisionprogramming from 10:00-11:00 P.M. on Tuesdays and Thursdays.Additionally, the television program Law & Order—SVU was the mostwatched television program and the NBA was the fifth most watchedtelevision program by the BF01 audience member. However, Law & Order SVUwas not necessarily watched from 8:00-9:00 P.M. on Mondays, Tuesdays,and Wednesdays. Similarly, the example table 700 includes behavior datafor the audience members DF01 and SF01.

In other examples, the table 700 may include other categories of programidentification data (e.g., actor names, show status information etc.).Further, in examples where the behavior processor 518 of FIG. 5 utilizesstatistical algorithms, the example table 700 may include weights and/orvalues associated with the behavior data. For example, the shows listedunder the Title category can be assigned a weight based on a frequencyrank of the show within the category. For example, Law and Order—SVUreceives a higher rank if the show was viewed more frequently than IronChef America. In other examples, the table 700 may also includecategories that correspond to Internet usage data (e.g., webpage name,webpage genre, keywords, etc.) and/or demographic data.

The table 700 of FIG. 7 is used by the example behavior processor 518 todetermine which of the audience members 116 is watching televisionprogramming. For example, during the second time period when the peoplemeter 110 is removed and/or deactivated, the base metering device 108determines program identification data viewed on a Tuesday at 9:35 P.M.with a title of Lost. The example behavior processor 518 first matchesthe day and time of the shown to all three of the audience members BF01,DF01, and SF01. The behavior processor 518 then determines that only theaudience members DF01 and SF01 match the program titles of Lost. As aresult, the behavior processor 518 determines that DF01 and SF01 arelikely currently watching the television program Lost.

In other examples, the behavior processor 518 analyzes the table 700 forviewing patterns to determine which of the audience members 116 arewatching programming during the second time period when there is not anexact match to the behavior data. For example, the behavior processor518 may determine that the audience member BF01 is viewing a collegehockey game based on the viewing patterns in the table 700 that indicateBF01 frequently watches sports including college basketball and the NBA.

In these other examples, the behavior processor 518 may accumulatevalues and/or weights for each matching item for the categories in thetable 700 for each of the audience members 116. The behavior processor518 then determines which of the audience members 116 are most likelywatching programming if the accumulated value for each audience member116 is greater than a predefined threshold. For example, if the behaviorprocessor 518 determines that program identification data for acurrently viewed program includes a program time of 8:00 P.M., a day ofMonday, a title of Fishing in Colorado, a genre of outdoor sports, and achannel of ESPN, the behavior processor 518 matches the programidentification data to the items within the table 700. The behaviorprocessor 518 may calculate the probability that the BF01 audiencemember is watching television by adding a weight from matching the8:00-9:00 P.M. time on a Monday, a weight from matching the genre ofsports, and a weight from matching the ESPN channel. The behaviorprocessor 518 may calculate the probability that the DF01 audiencemember is watching television by noting no items within the categorieslisted in table 700 match the audience identification data indicating aprobability of zero. The behavior processor 518 may also calculate theprobability that the SF01 audience member is watching television byadding a weight from matching the 8:00-9:00 P.M. time on a Monday. Thebehavior processor 518 may then determine that only the calculatedprobability for the BF01 audience member is greater than a threshold toindicate that the BF01 audience member is currently viewing thetelevision programming.

The behavior processor 518 of the illustrated example also uses thebehavior data shown in the table 700 to identify which of the audiencemembers 116 is using the computer 404 (and/or any other device capableof providing media content) to browse content on the Internet. Forexample, the behavior processor 518 may determine that SF01 is browsingthe Onion.com Internet site based on matching behavior data patterns tothe genre of Comedy and the program title of Family Guy. In yet otherexamples where the collection facility 124 of FIGS. 1-4 receives programidentification data directly from the service providers 118-122, thecollection facility 124 may use the behavior data within the table 700to identify which of the audience members 116 is watching television.Further, a portable and/or mailable meter that records programidentification data may also access and/or utilize the example table 700to determine which of the audience members 116 is watching television.

Flowcharts representative of example machine readable instructions forimplementing the base metering device 108 of FIGS. 1-5 are shown inFIGS. 8A, 8B, 9A, 9B, and 10. The machine readable instructions maycomprise a program for execution by a processor such as the processorP105 shown in the example processor platform P100 discussed below inconnection with FIG. 11. The program may be embodied in software storedon a computer readable medium such as a CD-ROM, a floppy disk, a harddrive, a digital versatile disk (DVD), or a memory associated with theprocessor P105, but the entire program and/or parts thereof couldalternatively be executed by a device other than the processor P105and/or embodied in firmware or dedicated hardware. Further, although theexample program is described with reference to the flowchartsillustrated in FIGS. 8A, 8B, 9A, 9B, and 10, many other methods ofimplementing the example base metering device 108 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined.

As mentioned above, the example instructions of FIGS. 8A, 8B, 9A, 9B,and 10 may be implemented using coded instructions (e.g., computerreadable instructions) stored on a tangible computer readable mediumsuch as a hard disk drive, a flash memory, a read-only memory (ROM), acompact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage media in whichinformation is stored for any duration (e.g., for extended time periods,permanently, brief instances, for temporarily buffering, and/or forcaching of the information). As used herein, the term tangible computerreadable medium is expressly defined to include any type of computerreadable storage and to exclude propagating signals. Additionally oralternatively, the example instructions of FIGS. 8A, 8B, 9A, 9B, and 10may be implemented using coded instructions (e.g., computer readableinstructions) stored on a non-transitory computer readable medium suchas a hard disk drive, a flash memory, a read-only memory, a compactdisk, a digital versatile disk, a cache, a random-access memory and/orany other storage media in which information is stored for any duration(e.g., for extended time periods, permanently, brief instances, fortemporarily buffering, and/or for caching of the information). As usedherein, the term non-transitory computer readable medium is expresslydefined to include any type of computer readable medium and to excludepropagating signals.

Example instructions 800 of FIGS. 8A and 8B collect behavior data duringa first time period using a people meter 110 and determine whichaudience members are viewing programming based on the behavior dataduring a second time period when the people meter 110 is removed.Multiple instances of the example instructions 800 may be executed inparallel or series for different households. Additionally, multipleinstances of the example instructions 800 may be executed in parallel orseries for different base metering devices 108 within the samehousehold.

The example instructions 800 of FIG. 8A begin by activating the peoplemeter 110 within the viewing area 112 of a house 114 (block 802). Theexample instructions 800 start a first time period for collectingaudience identification data and/or program identification data (e.g.,via the base metering device 108) (block 804). Next, the exampleinstructions 800 determine that at least one of the audience members 116is watching the television 104 (e.g., via the television programmingmeasurement circuitry 514) (block 806). The example instructions 800determine that at least one of the audience members is watchingprogramming by a television on/off detection algorithm and/or sensors(e.g., the sensors 516).

The example instructions 800 then collect program identification dataassociated with the viewed programming (e.g., via the televisionprogramming measurement circuitry 514) (block 808). While the program isbeing viewed, the example instructions 800 prompt the audience members116 to self-identify (e.g., via the people meter 110) (block 810). Theexample instructions 800 receive the prompt response as audienceidentification data from the audience members 116 self-identifying viathe people meter 110 (e.g., via the people meter interface 508) (block812). Next, the example instructions 800 combine the audienceidentification data and the program identification data similar to thedata shown in the table 600 of FIG. 6 for the current viewing session(e.g., via the behavior processor 518) (block 814). The exampleinstructions 800 also develop and/or generate behavior data from thecombined data similar to the data shown in the table 700 of FIG. 7(e.g., via the behavior processor 518) (block 814).

The example instructions 800 continue by determining if the first timeperiod has expired (e.g., via the timer 503) (block 816). If the firsttime period has not expired, the example instructions 800 return todetermining if at least one audience member is watching programming(block 806). However, if the first time period has expired, the exampleinstructions 800 transmit the collected program identification data,audience identification data, behavior data, and/or demographic data tothe collection facility 124 (e.g., via the processor 502 and/or thecommunication interface 506) (block 818). Alternatively, the exampleinstructions 800 may periodically transmit this data to the collectionfacility 124.

The example instructions 800 of FIG. 8B then determine if a demographicdata analysis is to be performed on the collected behavior data (e.g.,via the behavior processor 518) (block 820). If a demographic dataanalysis is to be performed, the example instructions 800 prompt theaudience members 116 for and subsequently receive demographic data(e.g., via the user interface 510) (block 822). The example instructions800 then combine the demographic data for each of the audience members116 with corresponding behavior data to generate audience compositiondata (e.g., via the behavior processor 518) (block 824). The exampleinstructions 800 use patterns within the audience composition data todetermine which of the audience members 116 is viewing programming whenthe people meter 110 is removed. In other examples, the demographic datamay be collected upon the start of the first time period.

Upon generating the audience composition data (block 824) and/or upondetermining a demographic data analysis is not to be performed (block820), the example instructions 800 deactivate and/or remove the peoplemeter 110 (e.g., via the people meter interface 508) (block 826). Theexample instructions 800 then start a second time period (e.g., via thetimer 503) (block 828). During this second time period, the exampleinstructions 800 collect program identification data without promptingthe audience members to self-identify.

The example instructions 800 of FIG. 8B continue by determining that atleast one of the audience members 116 is watching the television 104(e.g., via the television programming measurement circuitry 514) (block830). Next, the example instructions 800 collect program identificationdata associated with the viewed programming (e.g., via the televisionprogramming measurement circuitry 514) (block 832). The exampleinstructions 800 determine which of the audience members 116 is watchingthe programming (e.g., generate audience identification data) based onthe behavior data and/or the audience composition data collected duringthe first time period (e.g., via the behavior processor 518) (block834).

The example instructions 800 then determine if the second time periodhas expired (e.g., via the timer 503) (block 836). If the second timeperiod has not expired, the example instructions 800 return todetermining if at least one audience member is watching programming(block 830). However, if the second time period has expired, the exampleinstructions 800 transmit the collected program identification data,audience identification data, behavior data, and/or demographic data tothe collection facility 124 (e.g., via the processor 502 and/or thecommunication interface 506) (block 838). The example instructions 800then terminate. Alternatively, the example instructions 800 may continueto collect program identification data for a third time period and/orindefinitely.

The example instructions 900 of FIGS. 9A and 9B collect behavior dataduring a first time period from a first set of households using a peoplemeter and determine which audience members are viewing programming basedon the behavior data during a second time period when the people meteris removed. The example instructions 900 also collect behavior dataduring the second time period for a second set of households using thepeople meter 110 and adjust the data collected from the two groups atthe collection facility 124. Multiple instances of the exampleinstructions 900 may be executed in parallel or series for differenthouseholds. Additionally, multiple instances of the example instructions900 may be executed in parallel or series for different regions ofhousehold groups.

The example instructions 900 of FIG. 9A begin by activating and/orinstalling people meters 110 in a first set of active households (block902). The example instructions 900 then start a first time period (e.g.,via the timers 503) (block 904). During this first time period, theexample instructions 900 collect program identification data andaudience identification data for each of the households in the first set(e.g., via the people meters 110 and the base metering devices 108)(block 906). Next, the example instructions 900 generate behavior datafor each of the households within the first set based on the collecteddata (e.g., via the behavior processors 518) (block 908). The exampleinstructions 900 then end the first time period (e.g., via the timers503) (block 910) and transmit the behavior data, program identificationdata, and/or the audience identification data for each of the householdswithin the first set to the collection facility 124 (block 912).

The example instructions 900 then convert the first set of activehouseholds into legacy households by removing and/or deactivating thepeople meters 110 (block 914). At this point, the behavior datacollected during the first time period is used by the base meteringdevices 108 to determine which of the audience members 116 within eachof the households is watching television programming. Next, the exampleinstructions 900 of FIG. 9B activate people meters 110 and base meteringdevices 108 in a second set of active households that are different fromthe first set of active households (block 918). The example instructions900 then start a second time period (e.g., via the timers 503) (block920). The example instructions 900 then concurrently collect programidentification data from two different sets of households (e.g., alegacy set without people meters 110 and a second active set with peoplemeters 110).

For the legacy set of households, the example instructions 900 collectprogram identification data and determine which of the audience members116 is watching the programming based on behavior data from the firsttime period (e.g., via the base metering devices 108) (block 922). Theexample instructions 900 then transmit the program identification dataand the determined audience identification data to the collectionfacility 124 as legacy data (e.g., via the base metering device 108)(block 924). The example instructions 900 then adjust the legacy databased on previously received behavior data from the first time period(e.g., via the collection facility 124) (block 926).

For the second set of active households, the example instructions 900collect program identification data and audience identification data(e.g., via the base metering devices 108 and the people meters 110)(block 928). Next, the example instructions 900 generate behavior datafor each of the households within the second set based on the collecteddata (e.g., via the behavior processors 518) (block 930). The exampleinstructions 900 then transmit the behavior data, the programidentification data, and/or the audience identification data to thecollection facility 124 (e.g., via the base metering devices 108) (block932).

The example instructions 900 of FIG. 9B continue by generatingviewership statistics based on the adjusted legacy data and the datafrom the second set of active households (e.g., via the collectionfacility 124) (block 934). Additionally, in some examples, theinstructions 900 may adjust the legacy data by the data from the secondset of active households. The example instructions 900 then terminate.Alternatively, the example instructions 900 may continue to collectbehavior data, audience identification data, and/or programidentification data from the legacy households, households within thesecond set, and/or additional households.

The example instructions 1000 of FIG. 10 collect Internet usage dataduring a second time period based on behavior data collected during afirst time period with the people meter 110. Multiple instances of theexample instructions 1000 may be executed in parallel or series fordifferent households. Additionally, multiple instances of the exampleinstructions 1000 may be executed in parallel or series for differentbase metering devices 108 within the same household.

The example instructions 1000 of FIG. 10 begin by collecting and/orreceiving behavior data collected during a first time period (e.g., viathe base metering device 108 and/or the people meter 110) (block 1002).The behavior data, in some examples, may include Internet usage data andcorresponding audience identification data collected by the promptingaudience members 116 within a household to self-identify when they usethe Internet. The example instructions 1000 then start a second timeperiod (e.g., via the timer 503) (block 1004).

During the second time period, the example instructions 1000 maydetermine that at least one of the audience members 116 within themonitored household is navigating and/or browsing the Internet using thecomputer 404 (e.g., via the monitoring application 406 of FIG. 4) (block1006). Next, the example instructions 1000 collect Internet usage data(e.g., via the monitoring application 406) (block 1008). The exampleinstructions 1000 then determine an identity of the audience member 116by matching the Internet usage data to the behavior data (e.g., via themonitoring application 406 and/or the base metering device 108) (block1010). The example instructions 1000 also store the determined identityof the audience member 116 as audience identification data and store theInternet usage data (e.g., via the monitoring application 406 and/or thebase metering device 108) (block 1012).

The example instructions 1000 continue by determining if the second timeperiod has expired (e.g., via the timer 503) (block 1014). If the secondtime period has not expired, the example instructions 1000 return todetermining if at least one of the audience members 116 is navigatingthe Internet (block 1006). However, if the second time period hasexpired, the example instructions 1000 transmit the Internet usage dataand/or the audience identification data to the collection facility 124(block 1016). In other examples, the instructions 1000 may periodicallytransmit the Internet usage data and/or the audience identification datato the collection facility 124. The example instructions 1000 thenterminate. Alternatively, the example instructions 1000 may continue tocollect Internet usage data for a third time period and/or indefinitely.

FIG. 11 is a schematic diagram of an example processor platform P100capable of executing the instructions of FIGS. 8A, 8B, 9A, 9B, and/or 10to implement the example processor 502, the example memory 504, theexample communication interface 506, the example people meter interface508, the example user interface 510, the example display 512, theexample television programming measurement circuitry 514, the examplesensors 516, the example behavior processor 518, and/or more generally,the example base metering device 108 of FIGS. 1-5. For example, theprocessor platform P100 can be implemented by one or moregeneral-purpose processors, processor cores, microcontrollers, etc.

The processor platform P100 of the example of FIG. 11 includes at leastone general purpose programmable processor P105. The processor P105executes coded instructions P110 and/or P112 present in main memory ofthe processor P105 (e.g., within a RAM P115 and/or a ROM P120). Thecoded instructions P110 and/or P112 may be the instructions of FIGS. 8A,8B, 9A, 9B, and/or 10. The processor P105 may be any type of processingunit, such as a processor core, a processor and/or a microcontroller.The processor P105 may execute, among other things, the exampleinstructions of FIGS. 8A, 8B, 9A, 9B, and/or 10 to implement the examplemethods, articles of manufacture, and apparatus described herein.

The processor P105 is in communication with the main memory (including aROM P120 and/or the RAM P115) via a bus P125. The RAM P115 may beimplemented by DRAM, SDRAM, and/or any other type of RAM device, and ROMmay be implemented by flash memory and/or any other desired type ofmemory device. Access to the memory P115 and the memory P120 may becontrolled by a memory controller (not shown). One or both of theexample memories P115 and P120 may be used to implement the examplememory 504 of FIG. 5.

The processor platform P100 also includes an interface circuit P130. Theinterface circuit P130 may be implemented by any type of interfacestandard, such as an external memory interface, serial port,general-purpose input/output, etc. One or more input devices P135 andone or more output devices P140 are connected to the interface circuitP130.

Although certain example methods, apparatus and articles of manufacturehave been described herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. A method of collecting audience measurement datain a monitored household, the method comprising: collecting firstprogram identification data and audience identification data during afirst time period, the audience identification data being collected byprompting audience members in the monitored household to self-identifyusing a people meter; developing, with a first processor, audiencemember behavior data based on the first program identification data andthe audience identification data collected in the first time period;deactivating the people meter after the first time period; collectingsecond program identification data in a second time period after thefirst time period without collecting audience identification data; andidentifying, with at least one of the first processor or a secondprocessor, the audience members associated with the second programidentification data based on the audience member behavior data.
 2. Themethod of claim 1, wherein collecting the second program identificationdata without collecting the audience identification data comprises notprompting the audience members to self-identify.
 3. The method of claim1, wherein the first time period is more than one year and the secondtime period is more than one year.
 4. The method of claim 1, wherein theaudience member behavior data includes at least one of times of day,days of week, or programs typically watched by individual ones of theaudience members.
 5. The method of claim 1, wherein identifying theaudience members associated with the second program identification datafurther comprises obtaining demographic data for the identified audiencemembers and associating the demographic data with the programidentification data to generate audience composition data.
 6. The methodof claim 1, wherein the second time period extends a time for which themonitored household remains in a panel.
 7. The method of claim 1,wherein the second time period extends a panelist term for the monitoredhousehold by eliminating button pushing fatigue.
 8. The method of claim1, wherein the first program identification data is collected via a basemetering device and the second program identification data is collectedvia a mailable meter or a portable meter.
 9. The method of claim 1,wherein the first program identification data is collected via a basemetering device and the second program identification data is receivedfrom a service provider associated with the second programidentification data.
 10. The method of claim 1, further comprising:collecting Internet usage data during the second time period withoutcollecting audience identification data; and identifying the audiencemembers associated with the Internet usage data based on the audiencemember behavior data.
 11. A method of collecting audience measurementdata in a monitored household, the method comprising: collecting firstprogram identification data and audience identification data during afirst time period, the audience identification data being collected byprompting audience members in the monitored household to self-identifyusing a people meter; developing, with a first processor, audiencemember behavior data based on the first program identification data andthe audience identification data collected in the first time period;removing the people meter from the monitored household after the firsttime period; collecting second program identification data in a secondtime period after the first time period without collecting audienceidentification data; and identifying, with at least one of the firstprocessor or a second processor, the audience members associated withthe second program identification data based on the audience memberbehavior data.
 12. A system to collect audience measurement datacomprising: a set of active audience metering systems, each of theactive audience metering systems located in a respective panelisthousehold and including a respective meter to collect programidentification data and a respective people meter; and a set of legacyaudience metering systems, each of the legacy audience metering systemsformerly an active audience metering system and each of the legacyaudience metering systems located in a respective panelist household andincluding a respective meter to collect program identification data. 13.The system of claim 12, wherein each of the legacy audience meteringsystems at least one of (a) includes a deactivated people meter or (b)has no people meter.
 14. The system of claim 12, wherein audience memberbehavior data collected when the legacy audience metering systems wereactive audience metering systems is used to identify audience membersassociated with program identification data collected by the legacyaudience metering systems.
 15. The system of claim 12, wherein eachlegacy audience metering system was an active audience metering systemfor a first time period before becoming a legacy audience meteringsystem.
 16. The system of claim 15, wherein the first time period is atleast one year.
 17. The system of claim 12, wherein the programidentification data collected by the active audience metering systems isused for a first purpose, and the program identification data collectedby the legacy audience metering systems is used for a second purposedifferent from the first purpose.
 18. The system of claim 17, whereinthe first purpose is to generate first viewership statistics and thesecond purpose is to adjust the first viewership statistics.
 19. Thesystem of claim 12, further comprising a collection server to collectactive data from the active audience metering systems and legacy datafrom the legacy audience metering systems.
 20. The system of claim 19,wherein the collection server is to adjust the legacy data based on theactive data, and to combine the adjusted legacy data and the active datato generate viewership statistics.
 21. An apparatus to collect audiencemeasurement data comprising: a people meter interface to collectaudience identification data during a first time period, the audienceidentification data being collected by prompting audience members in amonitored household to self-identify using a people metercommunicatively coupled to the people meter interface, the people meterinterface to disable collection of the audience identification dataduring a second time period after the first time period; programmingmeasurement circuitry to collect first program identification dataduring the first time period and second program identification dataduring the second time period, the second program identification datacollected without collecting audience measurement data; and a behaviorprocessor to: develop audience member behavior data based on the firstprogram identification data and the audience identification datacollected in the first time period; and identify the audience membersassociated with the second program identification data based on theaudience member behavior data.
 22. The apparatus of claim 21, whereincollecting the second program identification data comprises notcollecting the audience identification data by not prompting theaudience members to self-identify.
 23. The apparatus of claim 21,further comprising a communication interface to collect Internet usagedata during the second time period without collecting audienceidentification data.
 24. The apparatus of claim 23, wherein the behaviorprocessor is to identify the audience members associated with theInternet usage data based on the audience member behavior data.
 25. Atangible machine-readable storage device or storage disc comprisinginstructions which machine accessible medium having instructions storedthereon that, when executed, cause a machine to at least: collect firstprogram identification data and audience identification data during afirst time period, the audience identification data being collected byprompting audience members in the monitored household to self-identifyusing a people meter; develop audience member behavior data based on thefirst program identification data and the audience identification datacollected in the first time period; deactivate the people meter afterthe first time period; collect second program identification data in asecond time period after the first time period without collectingaudience identification data; and identify the audience membersassociated with the second program identification data based on theaudience member behavior data.
 26. A machine-readable storage device orstorage disc as defined in claim 25, wherein the instructions, whenexecuted, cause the machine to collect the second program identificationdata without collecting the audience identification data by notprompting the audience members to self-identify.
 27. A machine-readablestorage device or storage disc as defined in claim 25, wherein theinstructions, when executed, cause the machine to identify the audiencemembers associated with the second program identification data furtherby obtaining demographic data for the identified audience members andassociating the demographic data with the program identification data togenerate audience composition data.
 28. A machine-readable storagedevice or storage disc as defined in claim 25, wherein the instructions,when executed, cause the machine to extend a time for which themonitored household remains in a panel.
 29. A machine-readable storagedevice or storage disc as defined in claim 25, wherein the instructions,when executed, cause the machine to extend a panelist term for themonitored household by eliminating button pushing fatigue.
 30. Amachine-readable storage device or storage disc as defined in claim 25,wherein the instructions, when executed, cause the machine to: collectInternet usage data during the second time period without collectingaudience identification data; and identify the audience membersassociated with the Internet usage data based on the audience memberbehavior data.